code: def test_language_model_data_collator():
      """
      Ensure `LanguageModelingDataCollator` works
      """
      norm_loader = MultiProcessDataLoader(
          MockDatasetReader(), "some path", batch_size=16
      )
      vocab = Vocabulary.from_instances(norm_loader.iter_instances())
      norm_loader.index_with(vocab)
      batch0 = list(norm_loader)[0]
  
      model_name = "epwalsh/bert-xsmall-dummy"
      data_collate = LanguageModelingDataCollator(model_name)
      mlm_loader = MultiProcessDataLoader(
          MockDatasetReader(),
          "some path",
          batch_size=16,
          collate_fn=data_collate,
      )
      vocab = Vocabulary.from_instances(mlm_loader.iter_instances())
      mlm_loader.index_with(vocab)
      batch1 = list(mlm_loader)[0]
  
      norm_inputs = batch0["source"]["tokens"]["token_ids"]
      mlm_inputs = batch1["source"]["tokens"]["token_ids"]
      mlm_labels = batch1["source"]["tokens"]["labels"]
  
      # if we replace the mlm inputs with their labels, should be same as origin inputs
      assert (
          torch.where(
              mlm_labels != -100, mlm_labels, mlm_inputs
          ).tolist()
          == norm_inputs.tolist()
      )
  
imports: from typing import List, Iterable, Dict
  import torch
  import pytest
  from allennlp.common.testing import requires_gpu
  from allennlp.data.instance import Instance
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.data.data_loaders import MultiProcessDataLoader, WorkerError
  from allennlp.data.fields import Field, TextField, MetadataField, TensorField
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.data_loaders.data_collator import LanguageModelingDataCollator
label_desc: Use image node_id 4 for calling a global function with example usage: test_language_model_data_collator() without return types